diff --git a/tools/sensitivity_analyze_results.py b/tools/sensitivity_analyze_results.py
index 07811236..960426e0 100755
--- a/tools/sensitivity_analyze_results.py
+++ b/tools/sensitivity_analyze_results.py
@@ -1,17 +1,12 @@
 #!/usr/bin/env python3
 
 # TODO:
-# - Add time argument (compare at specific time in log files)
 # - Have option to read in chemistry output (species amounts)
-# - Sort reactions by their effect on all quantities of interest
-# - Option to show e.g. box plots
-# - Show information about derivatives
 
 import numpy as np
 import matplotlib.pyplot as plt
 import argparse
 import pandas as pd
-import re
 
 parser = argparse.ArgumentParser(
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
@@ -21,6 +16,8 @@
                     help='Log files')
 parser.add_argument('-y', type=str, nargs='+', default=["sum(n_e)"],
                     help='y variable of the files to analyse')
+parser.add_argument('-time_index', type=int, default=-1,
+                    help='Which time index to consider')
 args = parser.parse_args()
 
 logs = sorted(args.logs)
@@ -35,25 +32,61 @@
 
     # Store all dataframes for a reaction index in a list
     if ix in all_cases:
-        all_cases[ix].append(df)
+        all_cases[ix].append((fac, df))
     else:
-        all_cases[ix] = [df]
+        all_cases[ix] = [(fac, df)]
 
 if 0 not in all_cases:
     raise ValueError('Base case not found (..._ix0000_...)')
 
-base_case = all_cases[0][0]
+base_case = all_cases[0][0][1]
+times = np.array(base_case['time'])
 reaction_ix = [ix for ix in all_cases.keys() if ix != 0]
 
-# Include base case in all cases
-for ix in reaction_ix:
-    all_cases[ix].append(base_case)
+effect_magnitudes = np.zeros(len(reaction_ix))
 
-print(f'{"  ix":4} {"quantity":15} {"     std/mean":15}')
+print(f'Using data at time t = {times[args.time_index]}')
 
-for y in args.y:
-    for ix in reaction_ix:
-        values = np.array([df[y] for df in all_cases[ix]])
-        means = np.mean(values[:, -1], axis=0)
-        std = np.std(values[:, -1], axis=0)
-        print(f'{ix:4} {y:15} {std/means:15.8f}')
+# Here mu indicates the mean derivative, mustar the mean absolute derivative,
+# and sigma the standard deviation in the derivative. All derivatives w.r.t. a
+# factor f.
+print(f'R{"#":<4} {"variable":15} {"mu":>15} {"mustar":>15} {"sigma":>15}')
+
+for i, ix in enumerate(reaction_ix):
+    values = np.array([df[args.y] for _, df in all_cases[ix]])
+    factors = np.array([f for f, _ in all_cases[ix]])
+
+    # Get values at time index
+    values = values[:, args.time_index]
+
+    # Get base values
+    base_values = np.array(base_case[args.y])[args.time_index]
+    base_values = np.tile(base_values, [len(factors), 1])
+
+    # Compute dg/df ~ (g(f) - g(1))/(f-1), where f is the factor centered
+    # around 1. E.g., if a factor is 1.1, the delta is 0.1
+    deltas = np.tile(factors - 1, [len(args.y), 1]).T
+    derivs = (values - base_values) / deltas
+
+    derivs_normalized = derivs / base_values
+    derivs_mean = np.mean(derivs_normalized, axis=0)
+    derivs_meanabs = np.mean(np.abs(derivs_normalized), axis=0)
+    derivs_sigma = np.std(derivs_normalized, axis=0)
+
+    for name, mu, mustar, sigma in zip(
+            args.y, derivs_mean, derivs_meanabs, derivs_sigma):
+        print(f'R{ix:<4} {name:15} {mu:15.8f} {mustar:15.8f} {sigma:15.8f}')
+
+    effect_magnitudes[i] = derivs_meanabs.max()
+
+print('\nReactions sorted by their overall importance:')
+
+# Load reaction names
+base_name = logs[0].replace('_log.txt', '')
+with open(base_name + '_reactions.txt', 'r') as f:
+    reactions_list = [x.strip() for x in f.readlines() if x.strip()]
+
+ix_sort = np.argsort(effect_magnitudes)[::-1]
+for i in ix_sort:
+    ix = reaction_ix[i]
+    print(f'R{ix:<4} {reactions_list[ix-1]:40} {effect_magnitudes[i]:<15.8f}')